Abstract
Earlier detection of patients with metastatic colorectal cancer (mCRC) might improve their treatment and survival outcomes. In this study, we used proton nuclear magnetic resonance ((1)H-NMR) to profile the serum metabolome in patients with mCRC and determine whether a disease signature may exist that is strong enough to predict overall survival (OS). In 153 patients with mCRC and 139 healthy subjects from three Danish hospitals, we profiled two independent sets of serum samples in a prospective phase II study. In the training set, (1)H-NMR metabolomic profiling could discriminate patients with mCRC from healthy subjects with a cross-validated accuracy of 100%. In the validation set, 96.7% of subjects were correctly classified. Patients from the training set with maximally divergent OS were chosen to construct an OS predictor. After validation, patients predicted to have short OS had significantly reduced survival (HR, 3.4; 95% confidence interval, 2.06-5.50; P = 1.33 × 10(-6)). A number of metabolites concurred with the (1)H-NMR fingerprint of mCRC, offering insights into mCRC metabolic pathways. Our findings establish that (1)H-NMR profiling of patient serum can provide a strong metabolomic signature of mCRC and that analysis of this signature may offer an independent tool to predict OS.
Highlights
Pattern recognition technologies in the omics world have been used for the diagnosis and prognosis of several tumor types using a variety of experimental platforms [1,2,3]
Projection to latent structure (PLS)-support vector machines (SVM) cross-validation analysis was repeated on a data set built with 96 healthy subjects and the 17 patients with metastatic colorectal cancer (mCRC) and ECOG-PS of 0, obtaining a classification accuracy of 100.0% on the training set and 86.4% on the validation set (Table 2), which indicates that a clear metabolic signature of the disease exists even in the serum of patients with mCRC and ECOG-PS of 0
By comparing the spectra of the serum samples of the underweight (N 1⁄4 7) and normal weight (N 1⁄4 71) versus overweight (N 1⁄4 52) and obese patients with mCRC (N 1⁄4 21), it appears that the overweight and obese patients are characterized by lower levels of formate (P 1⁄4 7.78 Â 10À3) and low-density lipoprotein (LDL)/high-density lipoprotein (HDL; P 1⁄4 1.42 Â 10À4) and higher levels of valine (P 1⁄4 1.98 Â 10À3), Nacetyl signal of glycoproteins (P 1⁄4 1.28 Â 10À2), CH-CH2-CO and CH2-CO signals due to lipids (P 1⁄4 1.24 Â 10À2; P 1⁄4 7.37 Â 10À3) and very LDL (VLDL; P 1⁄4 3.07 Â 10À2)
Summary
Pattern recognition technologies in the omics world have been used for the diagnosis and prognosis of several tumor types using a variety of experimental platforms [1,2,3]. In this study, conducted within the frame of the biomarker discovery activities of the FP7 project SPIDIA (www.spidia.eu), we conducted 1H-NMR profiling of serum samples collected from 153 Danish patients with mCRC before third-line treatment with cetuximab and irinotecan (Table 1 and Supplementary Table S1) and of serum samples from 139 healthy subjects (Table 1). The statistical model generated from 1H-NMR profiles of serum samples of subjects from 2 of the 3 hospitals can robustly discriminate healthy subjects (n 1⁄4 96) from patients with mCRC (n 1⁄4 45) with 100% cross-validated accuracy. Through multivariate analysis, a number of serum metabolites were identified whose levels were significantly different in patients with mCRC as compared to healthy subjects, as well as between patients with short and long OS These metabolites can provide hints both to define new biomarkers and to better understand the biochemistry involved in mCRC
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